• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Peer Review Process
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
ALEXMED ePosters
arrow Articles in Press
arrow Current Issue
Journal Archive
Volume Volume 7 (2025)
Volume Volume 6 (2024)
Volume Volume 5 (2023)
Issue Issue 4
Issue Issue 3
Issue Issue 2
Issue Issue 1
Volume Volume 4 (2022)
Volume Volume 3 (2021)
Volume Volume 2 (2020)
Volume Volume 1 (2019)
El-Saied Melies, M., El Agwany, A., Abdelhady, D., AbdElAziz, S. (2023). POSTOPERATIVE DETECTION OF RESIDUAL TUMOR IN ADVANCED OVARIAN CANCER BY ADVANCED ULTRASOUND VERSUS COMPUTED TOMOGRAGHY. ALEXMED ePosters, 5(2), 18-19. doi: 10.21608/alexpo.2023.214594.1614
Mahmoud El-Saied Melies; Ahmed Samy El Agwany; Dalia Abdrabelnabi Abdelhady; Samar AbdElAziz Mostafa AbdElAziz. "POSTOPERATIVE DETECTION OF RESIDUAL TUMOR IN ADVANCED OVARIAN CANCER BY ADVANCED ULTRASOUND VERSUS COMPUTED TOMOGRAGHY". ALEXMED ePosters, 5, 2, 2023, 18-19. doi: 10.21608/alexpo.2023.214594.1614
El-Saied Melies, M., El Agwany, A., Abdelhady, D., AbdElAziz, S. (2023). 'POSTOPERATIVE DETECTION OF RESIDUAL TUMOR IN ADVANCED OVARIAN CANCER BY ADVANCED ULTRASOUND VERSUS COMPUTED TOMOGRAGHY', ALEXMED ePosters, 5(2), pp. 18-19. doi: 10.21608/alexpo.2023.214594.1614
El-Saied Melies, M., El Agwany, A., Abdelhady, D., AbdElAziz, S. POSTOPERATIVE DETECTION OF RESIDUAL TUMOR IN ADVANCED OVARIAN CANCER BY ADVANCED ULTRASOUND VERSUS COMPUTED TOMOGRAGHY. ALEXMED ePosters, 2023; 5(2): 18-19. doi: 10.21608/alexpo.2023.214594.1614

POSTOPERATIVE DETECTION OF RESIDUAL TUMOR IN ADVANCED OVARIAN CANCER BY ADVANCED ULTRASOUND VERSUS COMPUTED TOMOGRAGHY

Article 1, Volume 5, Issue 2, April 2023, Page 18-19  XML
Document Type: Preliminary preprint short reports of original research
DOI: 10.21608/alexpo.2023.214594.1614
View on SCiNiTO View on SCiNiTO
Authors
Mahmoud El-Saied Melies1; Ahmed Samy El Agwanyorcid 2; Dalia Abdrabelnabi Abdelhady3; Samar AbdElAziz Mostafa AbdElAziz email 4
1Department of Obstetrics and Gynecology
2Department of Obstetrics and Gynecology, Faculty of Medicine, Alexandria University
3Department of Radiodiagnosis and Intervention, Alexandria Faculty of Medicine, Alexandria University
4Department of Obstetrics and Gynecology, Alexandria Faculty of Medicine, Alexandria University
Abstract
Compared to other cancers of the female reproductive system, ovarian cancer is the fifth most common cause of cancer-related death. Three-quarters of all cases of ovarian cancer at diagnosis are in stage III, and 26 percent are in stage IV. The complete resection of all visible macroscopic tumour during upfront cytoreductive surgery has been demonstrated to be the single most important prognostic factor in advanced stage disease. Amaximal cytoreduction followed by combined chemotherapy is the cornerstone treatment for these individuals. The primary and the best imaging method for preoperatively identifying adnexal masses is transvaginal ultrasonography. The most often used diagnostic method in clinical practise to evaluate the degree of tumour spread and the existence of peritoneal carcinomatosis is dedicated multidetector computed tomography procedures with standardised peritoneal carcer index forms.
AIM OF THE WORK:
To detect feasibility of advanced ultrasound by professional in the detection of postoperative residual tumor in cases of advanced ovarian cancer in comparison with computed tomography.
Keywords
Advanced Ovarian cancer; Cytoreductive surgery; Residual tumor
Supplementary Files
download 1614 22 (9).pdf
Statistics
Article View: 65
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Journal Management System. Designed by NotionWave.